OpenRaiser/Intern-Atlas huggingface dataset:
AgentRM: An OS-Inspired Resource Manager for LLM Agent Systems
Venue: arXiv cs.OS 2026
Year: 2026

Large Language Model (LLM) agent systems have experienced rapid adoption across diverse domains, yet they suffer from critical user experience problems that limit their practical deployment. Through an empirical analysis of over 40,000 GitHub issues from six major agent frameworks (OpenClaw, AutoGen, CrewAI, LangGraph, Codex, Claude Code), we identify two fundamental resource management challenges: (1) scheduling failures leading to system unresponsiveness due to blocking, zombie processes, and rate limit cascades, and (2) context degradation causing agent "amnesia" from unbounded memory growth and poor retention policies. Drawing inspiration from decades of operating systems research, we present AgentRM, a middleware resource manager that treats agent resources analogously to OS resources. AgentRM employs a Multi-Level Feedback Queue (MLFQ) scheduler with zombie reaping and rate-limit-aware admission control, coupled with a three-tier Context Lifecycle Manager that implements adaptive compaction and hibernation mechanisms. Our evaluation demonstrates significant improvements: AgentRM-MLFQ reduces P95 latency by 86%, decreases lane waste by 96%, and increases throughput by 168% while eliminating zombie agents (0 vs. 29 baseline). AgentRM-CLM achieves 100% key information retention with 95% quality score compared to 65.1% retention and 87% quality for existing approaches, albeit with higher compaction costs (34,330 vs. 17,212 tokens).
Defensible Design for OpenClaw: Securing Autonomous Tool-Invoking Agents
Venue: arXiv cs.CR 2026
Year: 2026

OpenClaw-like agents offer substantial productivity benefits, yet they are insecure by default because they combine untrusted inputs, autonomous action, extensibility, and privileged system access within a single execution loop. We use OpenClaw as an exemplar of a broader class of agents that interact with interfaces, manipulate files, invoke tools, and install extensions in real operating environments. Consequently, their security should be treated as a software engineering problem rather than as a product-specific concern. To address these architectural vulnerabilities, we propose a blueprint for defensible design. We present a risk taxonomy, secure engineering principles, and a practical research agenda to institutionalize safety in agent construction. Our goal is to transition the community focus from isolated vulnerability patching toward systematic defensive engineering and robust deployment practices.
Towards Faithful Multimodal Concept Bottleneck Models
Venue: arXiv cs.CV 2026
Year: 2026

Concept Bottleneck Models (CBMs) are interpretable models that route predictions through a layer of human-interpretable concepts. While widely studied in vision and, more recently, in NLP, CBMs remain largely unexplored in multimodal settings. For their explanations to be faithful, CBMs must satisfy two conditions: concepts must be properly detected, and concept representations must encode only their intended semantics, without smuggling extraneous task-relevant or inter-concept information into final predictions, a phenomenon known as leakage. Existing approaches treat concept detection and leakage mitigation as separate problems, and typically improve one at the expense of predictive accuracy. In this work, we introduce f-CBM, a faithful multimodal CBM framework built on a vision-language backbone that jointly targets both aspects through two complementary strategies: a differentiable leakage loss to mitigate leakage, and a Kolmogorov-Arnold Network prediction head that provides sufficient expressiveness to improve concept detection. Experiments demonstrate that f-CBM achieves the best trade-off between task accuracy, concept detection, and leakage reduction, while applying seamlessly to both image and text or text-only datasets, making it versatile across modalities.
Navig-AI-tion: Navigation by Contextual AI and Spatial Audio
Venue: arXiv cs.HC 2026
Year: 2026

Audio-only walking navigation can leave users disoriented, relying on vague cardinal directions and lacking real-time environmental context, leading to frequent errors. To address this, we present a novel system that integrates a Vision Language Model (VLM) with a spatial audio cue. Our system extracts environmental landmarks to anchor navigation instructions and, crucially, provides a directional spatial audio signal when the user faces the wrong direction, indicating the precise turn direction. In a user study (n=12), the spatial audio cue with VLM reduced route deviations compared to both VLM-only and Google Maps (audio-only) baseline systems. Users reported that the spatial audio cue effectively supported orientation and that landmark-anchored instructions provided a better navigation experience over audio-only Google Maps. This work serves as an initial look at the utility of future audio-only navigation systems for incorporating directional cues, especially real-time corrective spatial audio.
Out of Sight, Out of Mind? Evaluating State Evolution in Video World Models
Venue: arXiv cs.CV 2026
Year: 2026

Evolutions in the world, such as water pouring or ice melting, happen regardless of being observed. Video world models generate "worlds" via 2D frame observations. Can these generated "worlds" evolve regardless of observation? To probe this question, we design a benchmark to evaluate whether video world models can decouple state evolution from observation. Our benchmark, STEVO-Bench, applies observation control to evolving processes via instructions of occluder insertion, turning off the light, or specifying camera "lookaway" trajectories. By evaluating video models with and without camera control for a diverse set of naturally-occurring evolutions, we expose their limitations in decoupling state evolution from observation. STEVO-Bench proposes an evaluation protocol to automatically detect and disentangle failure modes of video world models across key aspects of natural state evolution. Analysis of STEVO-Bench results provide new insight into potential data and architecture bias of present-day video world models. Project website: https://glab-caltech.github.io/STEVOBench/. Blog: https://ziqi-ma.github.io/blog/2026/outofsight/
Representation Learning for Spatiotemporal Physical Systems
Venue: arXiv cs.LG 2026
Year: 2026

Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate emulator for the system's evolution in time. However, these emulators are computationally expensive to train and are subject to performance pitfalls, such as compounding errors during autoregressive rollout. In this work, we take a different perspective and look at scientific tasks further downstream of predicting the next frame, such as estimation of a system's governing physical parameters. Accuracy on these tasks offers a uniquely quantifiable glimpse into the physical relevance of the representations of these models. We evaluate the effectiveness of general-purpose self-supervised methods in learning physics-grounded representations that are useful for downstream scientific tasks. Surprisingly, we find that not all methods designed for physical modeling outperform generic self-supervised learning methods on these tasks, and methods that learn in the latent space (e.g., joint embedding predictive architectures, or JEPAs) outperform those optimizing pixel-level prediction objectives. Code is available at https://github.com/helenqu/physical-representation-learning.